raintrees: Rainforest tree data 1.

In SpatEntropy: Spatial Entropy Measures

Rainforest tree data 1.

Description

A marked point pattern dataset about four rainforest tree species: Acalypha diversifolia, Chamguava schippii, Inga pezizifera and Rinorea sylvatica

Usage

raintrees

Format

A ppp object (see package spatstat) with 7251 points, containing:

window

An object of type owin (see package spatstat), the 1000x500 metres observation area

x, y

Numeric vectors with points' coordinates

marks

A character vector matching the tree species to the data points

Details

This dataset documents the presence of tree species over Barro Colorado Island, Panama. Barro Colorado Island has been the focus of intensive research on lowland tropical rainforest since 1923 (http://www.ctfs.si.edu). Research identified several tree species over a rectangular observation window of size 1000x500 metres; the tree species constitute the point data categorical mark. This dataset presents 4 species with different spatial configurations: Acalypha diversifolia, Chamguava schippii, Inga pezizifera and Rinorea sylvatica. The overall dataset has a total number of 7251 points. The dataset is analyzed with spatial entropy measures in Altieri et al (2018) (references can be found at SpatEntropy).

Source

http://www.ctfs.si.edu

Examples

data(raintrees)
#plot(raintrees, main="", pch=16, cols=1:4)

#shannon's entropy of the four trees
shannon(raintrees)

#shannon's entropy of Z (tree pairs)
shannonZ(raintrees)

#leibovici's entropy
raintrees$window #to check size and unit of measurement
#example run on a subset of the data to speed up computations
subdata=raintrees[owin(c(0,200),c(0,100))]; plot(subdata)
outp=leibovici(subdata, ccdist=10, verbose=TRUE)
#do not worry about warnings like "data contain duplicated points": since
#coordinates are rounded to the first decimal place, it looks like
#some trees are overlapping when they are just very close.
#Entropy computation works properly anyway

#altieri's entropy
#example run on a subset of the data to speed up computations
subdata=raintrees[owin(c(0,200),c(0,100))]; plot(subdata)
outp=altieri(subdata, distbreak=c(1,2,5,10), verbose=TRUE)

#batty's entropy
#on all points, with a random partition in 10 sub-areas
batty.ent=batty(unmark(raintrees), partition=10)
#plot with partition
#plot(unmark(raintrees), pch=16, cex=0.6, main="")
#plot(batty.ent$area.tess, add=TRUE, border=2, lwd=2)
#on a specific tree species, with a random partition in 6 sub-areas
unique(marks(raintrees)) #to check the species' names
#plot(split.ppp(raintrees), main="") #to plot by species
batty.ent=batty(raintrees, category="cha2sc", partition=6)
#plot with partition
#plot(split.ppp(raintrees)$cha2sc, pch=16, cex=0.6, main="")
#plot(batty.ent$area.tess, add=TRUE, border=2)

#batty's entropy with a partition based on the covariate,
#exploiting spatstat functions
data(raintreesCOV)
#plot(raintreesCOV$grad, main="", col=gray(seq(1,0,l=100)))
data=split.ppp(raintrees)$acaldi
#plot(data, add=TRUE, pch=16, cex=0.6, main="")
#discretize the covariate
slopecut=cut(raintreesCOV$grad,
                breaks = quantile(raintreesCOV$grad, probs = (0:4)/4),
                labels = 1:4)
maskv=tiles=list()
for(ii in 1:nlevels(slopecut))
{
maskv[[ii]]=as.logical(c(slopecut$v)==levels(slopecut)[ii])
tiles[[ii]]=owin(xrange=data$window$xrange,
                 yrange=data$window$yrange,
                 mask=matrix(maskv[[ii]],nrow(slopecut$v)))
}
slopetess=list(tiles=tiles, n=nlevels(slopecut))
#plot(slopecut, main = "", col=gray(seq(1,0.4,l=4)))
#plot(data, add=TRUE, pch=16, cex=0.6, main="", col=1)

batty(data, partition=slopetess)

#karlstrom and ceccato's entropy
#on a specific tree species, with a random partition in 6 sub-areas
unique(marks(raintrees)) #to check the species' names
#plot(split.ppp(raintrees), main="") #to plot by species
KC.ent=karlstrom(raintrees, category="rinosy", partition=6, neigh=3)
#plot with partition
#plot(split.ppp(raintrees)$rinosy, pch=16, cex=0.6, main="")
#plot(KC.ent$area.tess, add=TRUE, border=2)

SpatEntropy documentation built on Nov. 17, 2023, 5:10 p.m.